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<li class="toctree-l1 current"><a class="current reference internal" href="#">Operators Supported</a></li>
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  <section id="operators-supported">
<span id="supported-ops"></span><h1>Operators Supported<a class="headerlink" href="#operators-supported" title="Permalink to this headline">¶</a></h1>
<section id="operators-currently-supported-through-converters">
<h2>Operators Currently Supported Through Converters<a class="headerlink" href="#operators-currently-supported-through-converters" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p>aten::_convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -&gt; (Tensor)</p></li>
<li><p>aten::_convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled) -&gt; (Tensor)</p></li>
<li><p>aten::abs(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::acos(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::acosh(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::adaptive_avg_pool1d(Tensor self, int[1] output_size) -&gt; (Tensor)</p></li>
<li><p>aten::adaptive_avg_pool2d(Tensor self, int[2] output_size) -&gt; (Tensor)</p></li>
<li><p>aten::adaptive_avg_pool3d(Tensor self, int[3] output_size) -&gt; (Tensor)</p></li>
<li><p>aten::adaptive_max_pool1d(Tensor self, int[2] output_size) -&gt; (Tensor, Tensor)</p></li>
<li><p>aten::adaptive_max_pool2d(Tensor self, int[2] output_size) -&gt; (Tensor, Tensor)</p></li>
<li><p>aten::adaptive_max_pool3d(Tensor self, int[3] output_size) -&gt; (Tensor, Tensor)</p></li>
<li><p>aten::add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -&gt; (Tensor)</p></li>
<li><p>aten::add.Tensor(Tensor self, Tensor other, Scalar alpha=1) -&gt; (Tensor)</p></li>
<li><p>aten::<a href="#id41"><span class="problematic" id="id42">add_</span></a>.Tensor(Tensor(a!) self, Tensor other, <a href="#id1"><span class="problematic" id="id2">*</span></a>, Scalar alpha=1) -&gt; (Tensor(a!))</p></li>
<li><p>aten::argmax(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor)</p></li>
<li><p>aten::argmin(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor)</p></li>
<li><p>aten::asin(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::asinh(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::atan(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::atanh(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::avg_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=[0], bool ceil_mode=False, bool count_include_pad=True) -&gt; (Tensor)</p></li>
<li><p>aten::avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=[0, 0], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -&gt; (Tensor)</p></li>
<li><p>aten::avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=[], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -&gt; (Tensor)</p></li>
<li><p>aten::batch_norm(Tensor input, Tensor? gamma, Tensor? beta, Tensor? mean, Tensor? var, bool training, float momentum, float eps, bool cudnn_enabled) -&gt; (Tensor)</p></li>
<li><p>aten::bitwise_not(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::bmm(Tensor self, Tensor mat2) -&gt; (Tensor)</p></li>
<li><p>aten::cat(Tensor[] tensors, int dim=0) -&gt; (Tensor)</p></li>
<li><p>aten::ceil(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::clamp(Tensor self, Scalar? min=None, Scalar? max=None) -&gt; (Tensor)</p></li>
<li><p>aten::clamp_max(Tensor self, Scalar max) -&gt; (Tensor)</p></li>
<li><p>aten::clamp_min(Tensor self, Scalar min) -&gt; (Tensor)</p></li>
<li><p>aten::constant_pad_nd(Tensor self, int[] pad, Scalar value=0) -&gt; (Tensor)</p></li>
<li><p>aten::cos(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::cosh(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::cumsum(Tensor self, int dim, <a href="#id3"><span class="problematic" id="id4">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
<li><p>aten::div.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
<li><p>aten::div.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
<li><p>aten::div.Tensor_mode(Tensor self, Tensor other, <a href="#id5"><span class="problematic" id="id6">*</span></a>, str? rounding_mode) -&gt; (Tensor)</p></li>
<li><p>aten::<a href="#id43"><span class="problematic" id="id44">div_</span></a>.Scalar(Tensor(a!) self, Scalar other) -&gt; (Tensor(a!))</p></li>
<li><p>aten::<a href="#id45"><span class="problematic" id="id46">div_</span></a>.Tensor(Tensor(a!) self, Tensor other) -&gt; (Tensor(a!))</p></li>
<li><p>aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -&gt; (Tensor)</p></li>
<li><p>aten::embedding(Tensor weight, Tensor indices, int padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -&gt; (Tensor)</p></li>
<li><p>aten::eq.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
<li><p>aten::eq.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
<li><p>aten::erf(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::exp(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::expand(Tensor(a) self, int[] size, <a href="#id7"><span class="problematic" id="id8">*</span></a>, bool implicit=False) -&gt; (Tensor(a))</p></li>
<li><p>aten::expand_as(Tensor(a) self, Tensor other) -&gt; (Tensor(a))</p></li>
<li><p>aten::fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -&gt; (Tensor)</p></li>
<li><p>aten::fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -&gt; (Tensor)</p></li>
<li><p>aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -&gt; (Tensor)</p></li>
<li><p>aten::floor(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::floor_divide(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
<li><p>aten::floor_divide.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
<li><p>aten::ge.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
<li><p>aten::ge.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
<li><p>aten::gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -&gt; (Tensor)</p></li>
<li><p>aten::gt.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
<li><p>aten::gt.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
<li><p>aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -&gt; (Tensor)</p></li>
<li><p>aten::hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -&gt; (Tensor(a!))</p></li>
<li><p>aten::index.Tensor(Tensor self, Tensor?[] indices) -&gt; (Tensor)</p></li>
<li><p>aten::instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, float momentum, float eps, bool cudnn_enabled) -&gt; (Tensor)</p></li>
<li><p>aten::layer_norm(Tensor input, int[] normalized_shape, Tensor? gamma, Tensor? beta, float eps, bool cudnn_enabled) -&gt; (Tensor)</p></li>
<li><p>aten::le.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
<li><p>aten::le.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
<li><p>aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -&gt; (Tensor)</p></li>
<li><p>aten::leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -&gt; (Tensor(a!))</p></li>
<li><p>aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -&gt; (Tensor)</p></li>
<li><p>aten::log(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -&gt; (Tensor, Tensor)</p></li>
<li><p>aten::lt.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
<li><p>aten::lt.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
<li><p>aten::masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -&gt; (Tensor)</p></li>
<li><p>aten::matmul(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
<li><p>aten::max(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::max.dim(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor values, Tensor indices)</p></li>
<li><p>aten::max.other(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
<li><p>aten::max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=[], int[1] dilation=[], bool ceil_mode=False) -&gt; (Tensor)</p></li>
<li><p>aten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=[0, 0], int[2] dilation=[1, 1], bool ceil_mode=False) -&gt; (Tensor)</p></li>
<li><p>aten::max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=[], int[3] dilation=[], bool ceil_mode=False) -&gt; (Tensor)</p></li>
<li><p>aten::mean(Tensor self, <a href="#id9"><span class="problematic" id="id10">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
<li><p>aten::mean.dim(Tensor self, int[] dim, bool keepdim=False, <a href="#id11"><span class="problematic" id="id12">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
<li><p>aten::min(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::min.dim(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor values, Tensor indices)</p></li>
<li><p>aten::min.other(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
<li><p>aten::mul.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
<li><p>aten::mul.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
<li><p>aten::<a href="#id47"><span class="problematic" id="id48">mul_</span></a>.Tensor(Tensor(a!) self, Tensor other) -&gt; (Tensor(a!))</p></li>
<li><p>aten::narrow(Tensor(a) self, int dim, int start, int length) -&gt; (Tensor(a))</p></li>
<li><p>aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, int length) -&gt; (Tensor(a))</p></li>
<li><p>aten::ne.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
<li><p>aten::ne.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
<li><p>aten::neg(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::norm.ScalarOpt_dim(Tensor self, Scalar? p, int[1] dim, bool keepdim=False) -&gt; (Tensor)</p></li>
<li><p>aten::permute(Tensor(a) self, int[] dims) -&gt; (Tensor(a))</p></li>
<li><p>aten::pixel_shuffle(Tensor self, int upscale_factor) -&gt; (Tensor)</p></li>
<li><p>aten::pow.Tensor_Scalar(Tensor self, Scalar exponent) -&gt; (Tensor)</p></li>
<li><p>aten::pow.Tensor_Tensor(Tensor self, Tensor exponent) -&gt; (Tensor)</p></li>
<li><p>aten::prelu(Tensor self, Tensor weight) -&gt; (Tensor)</p></li>
<li><p>aten::prod(Tensor self, <a href="#id13"><span class="problematic" id="id14">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
<li><p>aten::prod.dim_int(Tensor self, int dim, bool keepdim=False, <a href="#id15"><span class="problematic" id="id16">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
<li><p>aten::reciprocal(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::reflection_pad1d(Tensor self, int[2] padding) -&gt; (Tensor)</p></li>
<li><p>aten::reflection_pad2d(Tensor self, int[4] padding) -&gt; (Tensor)</p></li>
<li><p>aten::relu(Tensor input) -&gt; (Tensor)</p></li>
<li><p>aten::relu_(Tensor(a!) self) -&gt; (Tensor(a!))</p></li>
<li><p>aten::repeat(Tensor self, int[] repeats) -&gt; (Tensor)</p></li>
<li><p>aten::repeat_interleave.self_int(Tensor self, int repeats, int? dim=None, <a href="#id17"><span class="problematic" id="id18">*</span></a>, int? output_size=None) -&gt; (Tensor)</p></li>
<li><p>aten::replication_pad1d(Tensor self, int[2] padding) -&gt; (Tensor)</p></li>
<li><p>aten::replication_pad2d(Tensor self, int[4] padding) -&gt; (Tensor)</p></li>
<li><p>aten::replication_pad3d(Tensor self, int[6] padding) -&gt; (Tensor)</p></li>
<li><p>aten::reshape(Tensor self, int[] shape) -&gt; (Tensor)</p></li>
<li><p>aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -&gt; (Tensor)</p></li>
<li><p>aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -&gt; (Tensor)</p></li>
<li><p>aten::rsub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -&gt; (Tensor)</p></li>
<li><p>aten::scatter.src(Tensor self, int dim, Tensor index, Tensor src) -&gt; (Tensor)</p></li>
<li><p>aten::scatter.value(Tensor self, int dim, Tensor index, Scalar value) -&gt; (Tensor)</p></li>
<li><p>aten::select.int(Tensor(a) self, int dim, int index) -&gt; (Tensor(a))</p></li>
<li><p>aten::sigmoid(Tensor input) -&gt; (Tensor)</p></li>
<li><p>aten::sigmoid_(Tensor(a!) self) -&gt; (Tensor(a!))</p></li>
<li><p>aten::sin(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::sinh(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::slice.Tensor(Tensor(a) self, int dim=0, int? start=None, int? end=None, int step=1) -&gt; (Tensor(a))</p></li>
<li><p>aten::softmax.int(Tensor self, int dim, int? dtype=None) -&gt; (Tensor)</p></li>
<li><p>aten::split(Tensor self, int[] split_sizes, int dim=0) -&gt; (Tensor[])</p></li>
<li><p>aten::split.Tensor(Tensor(a) self, int split_size, int dim=0) -&gt; (Tensor[])</p></li>
<li><p>aten::split.sizes(Tensor(a -&gt; <a href="#id19"><span class="problematic" id="id20">*</span></a>) self, int[] split_size, int dim=0) -&gt; (Tensor[])</p></li>
<li><p>aten::split_with_sizes(Tensor(a) self, int[] split_sizes, int dim=0) -&gt; (Tensor[])</p></li>
<li><p>aten::sqrt(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::square(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::squeeze.dim(Tensor(a) self, int dim) -&gt; (Tensor(a))</p></li>
<li><p>aten::stack(Tensor[] tensors, int dim=0) -&gt; (Tensor)</p></li>
<li><p>aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -&gt; (Tensor)</p></li>
<li><p>aten::sub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -&gt; (Tensor)</p></li>
<li><p>aten::<a href="#id49"><span class="problematic" id="id50">sub_</span></a>.Tensor(Tensor(a!) self, Tensor other, <a href="#id21"><span class="problematic" id="id22">*</span></a>, Scalar alpha=1) -&gt; (Tensor(a!))</p></li>
<li><p>aten::sum(Tensor self, <a href="#id23"><span class="problematic" id="id24">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
<li><p>aten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, <a href="#id25"><span class="problematic" id="id26">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
<li><p>aten::t(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::tan(Tensor self) -&gt; (Tensor)</p></li>
<li><p>aten::tanh(Tensor input) -&gt; (Tensor)</p></li>
<li><p>aten::tanh_(Tensor(a!) self) -&gt; (Tensor(a!))</p></li>
<li><p>aten::to.device(Tensor(a) self, Device device, int dtype, bool non_blocking=False, bool copy=False, int? memory_format=None) -&gt; (Tensor(a))</p></li>
<li><p>aten::to.dtype(Tensor self, int dtype, bool non_blocking=False, bool copy=False, int? memory_format=None) -&gt; (Tensor)</p></li>
<li><p>aten::to.other(Tensor self, Tensor other, bool non_blocking=False, bool copy=False, int? memory_format=None) -&gt; (Tensor)</p></li>
<li><p>aten::to.prim_Device(Tensor(a) self, Device? device, int? dtype=None, bool non_blocking=False, bool copy=False) -&gt; (Tensor(a|b))</p></li>
<li><p>aten::topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -&gt; (Tensor values, Tensor indices)</p></li>
<li><p>aten::transpose.int(Tensor(a) self, int dim0, int dim1) -&gt; (Tensor(a))</p></li>
<li><p>aten::unbind.int(Tensor(a -&gt; <a href="#id27"><span class="problematic" id="id28">*</span></a>) self, int dim=0) -&gt; (Tensor[])</p></li>
<li><p>aten::unsqueeze(Tensor(a) self, int dim) -&gt; (Tensor(a))</p></li>
<li><p>aten::upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -&gt; (Tensor)</p></li>
<li><p>aten::upsample_bilinear2d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -&gt; (Tensor)</p></li>
<li><p>aten::upsample_linear1d(Tensor self, int[1] output_size, bool align_corners, float? scales=None) -&gt; (Tensor)</p></li>
<li><p>aten::upsample_linear1d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -&gt; (Tensor)</p></li>
<li><p>aten::upsample_nearest1d(Tensor self, int[1] output_size, float? scales=None) -&gt; (Tensor)</p></li>
<li><p>aten::upsample_nearest1d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -&gt; (Tensor)</p></li>
<li><p>aten::upsample_nearest2d(Tensor self, int[2] output_size, float? scales_h=None, float? scales_w=None) -&gt; (Tensor)</p></li>
<li><p>aten::upsample_nearest2d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -&gt; (Tensor)</p></li>
<li><p>aten::upsample_nearest3d(Tensor self, int[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -&gt; (Tensor)</p></li>
<li><p>aten::upsample_nearest3d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -&gt; (Tensor)</p></li>
<li><p>aten::upsample_trilinear3d(Tensor self, int[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -&gt; (Tensor)</p></li>
<li><p>aten::upsample_trilinear3d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -&gt; (Tensor)</p></li>
<li><p>aten::view(Tensor(a) self, int[] size) -&gt; (Tensor(a))</p></li>
<li><p>trt::const(Tensor self) -&gt; (Tensor)</p></li>
</ul>
</section>
<section id="operators-currently-supported-through-evaluators">
<h2>Operators Currently Supported Through Evaluators<a class="headerlink" href="#operators-currently-supported-through-evaluators" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p>aten::Bool.float(float b) -&gt; (bool)</p></li>
<li><p>aten::Bool.int(int a) -&gt; (bool)</p></li>
<li><p>aten::Float.Scalar(Scalar a) -&gt; float</p></li>
<li><p>aten::Float.bool(bool a) -&gt; float</p></li>
<li><p>aten::Float.int(int a) -&gt; float</p></li>
<li><p>aten::Int.Scalar(Scalar a) -&gt; int</p></li>
<li><p>aten::Int.bool(bool a) -&gt; int</p></li>
<li><p>aten::Int.float(float a) -&gt; int</p></li>
<li><p>aten::Int.int(int a) -&gt; int</p></li>
<li><p>aten::__and__(int a, int b) -&gt; (bool)</p></li>
<li><p>aten::__and__.bool(bool a, bool b) -&gt; (bool)</p></li>
<li><p>aten::__derive_index(int idx, int start, int step) -&gt; int</p></li>
<li><p>aten::__getitem__.t(t[](a) list, int idx) -&gt; (t(*))</p></li>
<li><p>aten::__is__(t1 self, t2 obj) -&gt; bool</p></li>
<li><p>aten::__isnot__(t1 self, t2 obj) -&gt; bool</p></li>
<li><p>aten::__not__(bool self) -&gt; bool</p></li>
<li><p>aten::__or__(int a, int b) -&gt; (bool)</p></li>
<li><p>aten::__range_length(int lo, int hi, int step) -&gt; int</p></li>
<li><p>aten::__round_to_zero_floordiv(int a, int b) -&gt; (int)</p></li>
<li><p>aten::__xor__(int a, int b) -&gt; (bool)</p></li>
<li><p>aten::add.float(float a, float b) -&gt; (float)</p></li>
<li><p>aten::add.int(int a, int b) -&gt; (int)</p></li>
<li><p>aten::add.str(str a, str b) -&gt; (str)</p></li>
<li><p>aten::<a href="#id51"><span class="problematic" id="id52">add_</span></a>.t(t[](a!) self, t[] b) -&gt; (t[])</p></li>
<li><p>aten::append.t(t[](a!) self, t(c -&gt; <a href="#id29"><span class="problematic" id="id30">*</span></a>) el) -&gt; (t[](a!))</p></li>
<li><dl class="simple">
<dt>aten::arange(Scalar end, <a href="#id31"><span class="problematic" id="id32">*</span></a>, int? dtype=None, int? layout=None,</dt><dd><p>Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>aten::arange.start(Scalar start, Scalar end, <a href="#id33"><span class="problematic" id="id34">*</span></a>, ScalarType? dtype=None,</dt><dd><p>Layout? layout=None, Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>aten::arange.start_step(Scalar start, Scalar end, Scalar step, <a href="#id35"><span class="problematic" id="id36">*</span></a>, ScalarType? dtype=None,</dt><dd><p>Layout? layout=None, Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
</dd>
</dl>
</li>
<li><p>aten::clone(Tensor self, <a href="#id37"><span class="problematic" id="id38">*</span></a>, int? memory_format=None) -&gt; (Tensor)</p></li>
<li><p>aten::copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -&gt; (Tensor(a!))</p></li>
<li><p>aten::dim(Tensor self) -&gt; int</p></li>
<li><p>aten::div.float(float a, float b) -&gt; (float)</p></li>
<li><p>aten::div.int(int a, int b) -&gt; (float)</p></li>
<li><p>aten::eq.bool(bool a, bool b) -&gt; (bool)</p></li>
<li><p>aten::eq.float(float a, float b) -&gt; (bool)</p></li>
<li><p>aten::eq.float_int(float a, int b) -&gt; (bool)</p></li>
<li><p>aten::eq.int(int a, int b) -&gt; (bool)</p></li>
<li><p>aten::eq.int_float(int a, float b) -&gt; (bool)</p></li>
<li><p>aten::eq.str(str a, str b) -&gt; (bool)</p></li>
<li><p>aten::extend.t(t[](a!) self, t[] other) -&gt; ()</p></li>
<li><p>aten::floor.float(float a) -&gt; (int)</p></li>
<li><p>aten::floor.int(int a) -&gt; (int)</p></li>
<li><p>aten::floordiv.float(float a, float b) -&gt; (int)</p></li>
<li><p>aten::floordiv.int(int a, int b) -&gt; (int)</p></li>
<li><p>aten::format(str self, …) -&gt; (str)</p></li>
<li><p>aten::ge.bool(bool a, bool b) -&gt; (bool)</p></li>
<li><p>aten::ge.float(float a, float b) -&gt; (bool)</p></li>
<li><p>aten::ge.float_int(float a, int b) -&gt; (bool)</p></li>
<li><p>aten::ge.int(int a, int b) -&gt; (bool)</p></li>
<li><p>aten::ge.int_float(int a, float b) -&gt; (bool)</p></li>
<li><p>aten::gt.bool(bool a, bool b) -&gt; (bool)</p></li>
<li><p>aten::gt.float(float a, float b) -&gt; (bool)</p></li>
<li><p>aten::gt.float_int(float a, int b) -&gt; (bool)</p></li>
<li><p>aten::gt.int(int a, int b) -&gt; (bool)</p></li>
<li><p>aten::gt.int_float(int a, float b) -&gt; (bool)</p></li>
<li><p>aten::is_floating_point(Tensor self) -&gt; (bool)</p></li>
<li><p>aten::le.bool(bool a, bool b) -&gt; (bool)</p></li>
<li><p>aten::le.float(float a, float b) -&gt; (bool)</p></li>
<li><p>aten::le.float_int(float a, int b) -&gt; (bool)</p></li>
<li><p>aten::le.int(int a, int b) -&gt; (bool)</p></li>
<li><p>aten::le.int_float(int a, float b) -&gt; (bool)</p></li>
<li><p>aten::len.t(t[] a) -&gt; (int)</p></li>
<li><p>aten::lt.bool(bool a, bool b) -&gt; (bool)</p></li>
<li><p>aten::lt.float(float a, float b) -&gt; (bool)</p></li>
<li><p>aten::lt.float_int(float a, int b) -&gt; (bool)</p></li>
<li><p>aten::lt.int(int a, int b) -&gt; (bool)</p></li>
<li><p>aten::lt.int_float(int a, float b) -&gt; (bool)</p></li>
<li><p>aten::mul.float(float a, float b) -&gt; (float)</p></li>
<li><p>aten::mul.int(int a, int b) -&gt; (int)</p></li>
<li><p>aten::ne.bool(bool a, bool b) -&gt; (bool)</p></li>
<li><p>aten::ne.float(float a, float b) -&gt; (bool)</p></li>
<li><p>aten::ne.float_int(float a, int b) -&gt; (bool)</p></li>
<li><p>aten::ne.int(int a, int b) -&gt; (bool)</p></li>
<li><p>aten::ne.int_float(int a, float b) -&gt; (bool)</p></li>
<li><p>aten::neg.int(int a) -&gt; (int)</p></li>
<li><p>aten::numel(Tensor self) -&gt; int</p></li>
<li><p>aten::pow.float(float a, float b) -&gt; (float)</p></li>
<li><p>aten::pow.float_int(float a, int b) -&gt; (float)</p></li>
<li><p>aten::pow.int(int a, int b) -&gt; (float)</p></li>
<li><p>aten::pow.int_float(int a, float b) -&gt; (float)</p></li>
<li><p>aten::size(Tensor self) -&gt; (int[])</p></li>
<li><p>aten::size.int(Tensor self, int dim) -&gt; (int)</p></li>
<li><p>aten::slice.t(t[] l, int start, int end=9223372036854775807, int step=1) -&gt; (t[])</p></li>
<li><p>aten::sqrt.float(float a) -&gt; (float)</p></li>
<li><p>aten::sqrt.int(int a) -&gt; (float)</p></li>
<li><p>aten::sub.float(float a, float b) -&gt; (float)</p></li>
<li><p>aten::sub.int(int a, int b) -&gt; (int)</p></li>
<li><p>aten::tensor(t[] data, <a href="#id39"><span class="problematic" id="id40">*</span></a>, int? dtype=None, Device? device=None, bool requires_grad=False) -&gt; (Tensor)</p></li>
<li><p>prim::TupleIndex(Any tup, int i) -&gt; (Any)</p></li>
<li><p>prim::dtype(Tensor a) -&gt; (int)</p></li>
<li><p>prim::max.bool(bool a, bool b) -&gt; (bool)</p></li>
<li><p>prim::max.float(float a, float b) -&gt; (bool)</p></li>
<li><p>prim::max.float_int(float a, int b) -&gt; (bool)</p></li>
<li><p>prim::max.int(int a, int b) -&gt; (bool)</p></li>
<li><p>prim::max.int_float(int a, float b) -&gt; (bool)</p></li>
<li><p>prim::max.self_int(int[] self) -&gt; (int)</p></li>
<li><p>prim::min.bool(bool a, bool b) -&gt; (bool)</p></li>
<li><p>prim::min.float(float a, float b) -&gt; (bool)</p></li>
<li><p>prim::min.float_int(float a, int b) -&gt; (bool)</p></li>
<li><p>prim::min.int(int a, int b) -&gt; (bool)</p></li>
<li><p>prim::min.int_float(int a, float b) -&gt; (bool)</p></li>
<li><p>prim::min.self_int(int[] self) -&gt; (int)</p></li>
<li><p>prim::shape(Tensor a) -&gt; (int[])</p></li>
</ul>
</section>
</section>


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              <ul>
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